It is likely in real-world applications that only little data is available for training a knowledge-based system. We present a method for automatically training the knowledge-representing membership functions of a Fuzzy-Pattern-Classification system that works also when only little data is available and the universal set is described insufficiently. Actually, this paper presents how the Modified-Fuzzy-Pattern-Classifier’s membership functions are trained using probability distribution functions.


Fuzzy Logic Probability Theory Fuzzy-Pattern-Classification Machine Learning Artificial Intelligence Pattern Recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Uwe Mönks
    • 1
  • Denis Petker
    • 2
  • Volker Lohweg
    • 1
  1. 1.inIT – Institute Industrial ITOstwestfalen-Lippe University of Applied SciencesLemgoGermany
  2. 2.OWITA GmbHLemgoGermany

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